Let \(A^{K+2}\)  be the matrix \(A^{K}\)  augmented with two rows and two columns of zeros.
We pad the matrix to account for information on the scene representing the two privacy classes to be encoded before learning the graph. This information is provided by combining prior knowledge with information for each image during training.
Unlike in GIP [1]}, in GPA, the matrix \(A\)  does not encode prior information on the privacy class of \(K\)  object categories.
The initalisation of GPA addresses biased information relating to the long-tail distribution [2]} of object categories found in the dataset by considering object co-occurrences instead of the frequency of occurrence (recurrence) of objects.
